# DeepSeek-V3.2 ## Introduction DeepSeek-V3.2 is a sparse attention model. The main architecture is similar to DeepSeek-V3.1, but with a sparse attention mechanism, which is designed to explore and validate optimizations for training and inference efficiency in long-context scenarios. This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation. ## Supported Features Refer to [supported features](../../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix. Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the feature's configuration. ## Environment Preparation ### Model Weight - `DeepSeek-V3.2-Exp`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-BF16) - `DeepSeek-V3.2-Exp-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-w8a8) - `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Model weight in BF16 not found now. - `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.2-W8A8/) It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`. ### Verify Multi-node Communication(Optional) If you want to deploy multi-node environment, you need to verify multi-node communication according to [verify multi-node communication environment](../../installation.md#verify-multi-node-communication). ### Installation You can use our official docker image to run `DeepSeek-V3.2` directly. :::::{tab-set} :sync-group: install ::::{tab-item} A3 series :sync: A3 Start the docker image on your each node. ```{code-block} bash :substitutions: export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3 docker run --rm \ --name vllm-ascend \ --shm-size=1g \ --net=host \ --device /dev/davinci0 \ --device /dev/davinci1 \ --device /dev/davinci2 \ --device /dev/davinci3 \ --device /dev/davinci4 \ --device /dev/davinci5 \ --device /dev/davinci6 \ --device /dev/davinci7 \ --device /dev/davinci8 \ --device /dev/davinci9 \ --device /dev/davinci10 \ --device /dev/davinci11 \ --device /dev/davinci12 \ --device /dev/davinci13 \ --device /dev/davinci14 \ --device /dev/davinci15 \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /root/.cache:/root/.cache \ -it $IMAGE bash ``` :::: ::::{tab-item} A2 series :sync: A2 Start the docker image on your each node. ```{code-block} bash :substitutions: export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version| docker run --rm \ --name vllm-ascend \ --shm-size=1g \ --net=host \ --device /dev/davinci0 \ --device /dev/davinci1 \ --device /dev/davinci2 \ --device /dev/davinci3 \ --device /dev/davinci4 \ --device /dev/davinci5 \ --device /dev/davinci6 \ --device /dev/davinci7 \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /root/.cache:/root/.cache \ -it $IMAGE bash ``` :::: ::::: In addition, if you don't want to use the docker image as above, you can also build all from source: - Install `vllm-ascend` from source, refer to [installation](../../installation.md). If you want to deploy multi-node environment, you need to set up environment on each node. ## Deployment :::{note} In this tutorial, we suppose you downloaded the model weight to `/root/.cache/`. Feel free to change it to your own path. ::: ### Single-node Deployment - Quantized model `DeepSeek-V3.2-w8a8` can be deployed on 1 Atlas 800 A3 (64G × 16). Run the following script to execute online inference. ```shell export HCCL_OP_EXPANSION_MODE="AIV" export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export VLLM_USE_V1=1 export HCCL_BUFFSIZE=200 export VLLM_ASCEND_ENABLE_MLAPO=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \ --host 0.0.0.0 \ --port 8000 \ --data-parallel-size 2 \ --tensor-parallel-size 8 \ --quantization ascend \ --seed 1024 \ --served-model-name deepseek_v3_2 \ --enable-expert-parallel \ --max-num-seqs 16 \ --max-model-len 8192 \ --max-num-batched-tokens 4096 \ --trust-remote-code \ --no-enable-prefix-caching \ --gpu-memory-utilization 0.92 \ --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' ``` ### Multi-node Deployment - `DeepSeek-V3.2-w8a8`: require at least 2 Atlas 800 A2 (64G × 8). Run the following scripts on two nodes respectively. :::::{tab-set} :sync-group: install ::::{tab-item} A3 series :sync: A3 **Node0** ```{code-block} bash :substitutions: # this obtained through ifconfig # nic_name is the network interface name corresponding to local_ip of the current node nic_name="xxx" local_ip="xxx" # The value of node0_ip must be consistent with the value of local_ip set in node0 (master node) node0_ip="xxxx" export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export VLLM_USE_V1=1 export HCCL_BUFFSIZE=200 export VLLM_ASCEND_ENABLE_MLAPO=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \ --host 0.0.0.0 \ --port 8077 \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --data-parallel-address $node0_ip \ --data-parallel-rpc-port 12890 \ --tensor-parallel-size 16 \ --quantization ascend \ --seed 1024 \ --served-model-name deepseek_v3_2 \ --enable-expert-parallel \ --max-num-seqs 16 \ --max-model-len 8192 \ --max-num-batched-tokens 4096 \ --trust-remote-code \ --no-enable-prefix-caching \ --gpu-memory-utilization 0.92 \ --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' ``` **Node1** ```{code-block} bash :substitutions: # this obtained through ifconfig # nic_name is the network interface name corresponding to local_ip of the current node nic_name="xxx" local_ip="xxx" # The value of node0_ip must be consistent with the value of local_ip set in node0 (master node) node0_ip="xxxx" export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export VLLM_USE_V1=1 export HCCL_BUFFSIZE=200 export VLLM_ASCEND_ENABLE_MLAPO=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \ --host 0.0.0.0 \ --port 8077 \ --headless \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --data-parallel-start-rank 1 \ --data-parallel-address $node0_ip \ --data-parallel-rpc-port 12890 \ --tensor-parallel-size 16 \ --quantization ascend \ --seed 1024 \ --served-model-name deepseek_v3_2 \ --enable-expert-parallel \ --max-num-seqs 16 \ --max-model-len 8192 \ --max-num-batched-tokens 4096 \ --trust-remote-code \ --no-enable-prefix-caching \ --gpu-memory-utilization 0.92 \ --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' ``` :::: ::::{tab-item} A2 series :sync: A2 **Node0** ```{code-block} bash :substitutions: # this obtained through ifconfig # nic_name is the network interface name corresponding to local_ip of the current node nic_name="xxx" local_ip="xxx" # The value of node0_ip must be consistent with the value of local_ip set in node0 (master node) node0_ip="xxxx" export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export HCCL_BUFFSIZE=200 export VLLM_ASCEND_ENABLE_MLAPO=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 export HCCL_CONNECT_TIMEOUT=120 export HCCL_INTRA_PCIE_ENABLE=1 export HCCL_INTRA_ROCE_ENABLE=0 export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \ --host 0.0.0.0 \ --port 8077 \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --data-parallel-address $node0_ip \ --data-parallel-rpc-port 13389 \ --tensor-parallel-size 8 \ --quantization ascend \ --seed 1024 \ --served-model-name deepseek_v3_2 \ --enable-expert-parallel \ --max-num-seqs 16 \ --max-model-len 8192 \ --max-num-batched-tokens 4096 \ --trust-remote-code \ --no-enable-prefix-caching \ --gpu-memory-utilization 0.92 \ --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[8, 16, 24, 32, 40, 48]}' \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' ``` **Node1** ```{code-block} bash :substitutions: # this obtained through ifconfig # nic_name is the network interface name corresponding to local_ip of the current node nic_name="xxx" local_ip="xxx" # The value of node0_ip must be consistent with the value of local_ip set in node0 (master node) node0_ip="xxxx" export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export HCCL_BUFFSIZE=200 export VLLM_ASCEND_ENABLE_MLAPO=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 export HCCL_CONNECT_TIMEOUT=120 export HCCL_INTRA_PCIE_ENABLE=1 export HCCL_INTRA_ROCE_ENABLE=0 export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \ --host 0.0.0.0 \ --port 8077 \ --headless \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --data-parallel-start-rank 1 \ --data-parallel-address $node0_ip \ --data-parallel-rpc-port 13389 \ --tensor-parallel-size 8 \ --quantization ascend \ --seed 1024 \ --served-model-name deepseek_v3_2 \ --enable-expert-parallel \ --max-num-seqs 16 \ --max-model-len 8192 \ --max-num-batched-tokens 4096 \ --trust-remote-code \ --no-enable-prefix-caching \ --gpu-memory-utilization 0.92 \ --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[8, 16, 24, 32, 40, 48]}' \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' ``` :::: ::::: ### Prefill-Decode Disaggregation We'd like to show the deployment guide of `DeepSeek-V3.2` on multi-node environment with 1P1D for better performance. Before you start, please 1. prepare the script `launch_online_dp.py` on each node: ```python import argparse import multiprocessing import os import subprocess import sys def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--dp-size", type=int, required=True, help="Data parallel size." ) parser.add_argument( "--tp-size", type=int, default=1, help="Tensor parallel size." ) parser.add_argument( "--dp-size-local", type=int, default=-1, help="Local data parallel size." ) parser.add_argument( "--dp-rank-start", type=int, default=0, help="Starting rank for data parallel." ) parser.add_argument( "--dp-address", type=str, required=True, help="IP address for data parallel master node." ) parser.add_argument( "--dp-rpc-port", type=str, default=12345, help="Port for data parallel master node." ) parser.add_argument( "--vllm-start-port", type=int, default=9000, help="Starting port for the engine." ) return parser.parse_args() args = parse_args() dp_size = args.dp_size tp_size = args.tp_size dp_size_local = args.dp_size_local if dp_size_local == -1: dp_size_local = dp_size dp_rank_start = args.dp_rank_start dp_address = args.dp_address dp_rpc_port = args.dp_rpc_port vllm_start_port = args.vllm_start_port def run_command(visible_devices, dp_rank, vllm_engine_port): command = [ "bash", "./run_dp_template.sh", visible_devices, str(vllm_engine_port), str(dp_size), str(dp_rank), dp_address, dp_rpc_port, str(tp_size), ] subprocess.run(command, check=True) if __name__ == "__main__": template_path = "./run_dp_template.sh" if not os.path.exists(template_path): print(f"Template file {template_path} does not exist.") sys.exit(1) processes = [] num_cards = dp_size_local * tp_size for i in range(dp_size_local): dp_rank = dp_rank_start + i vllm_engine_port = vllm_start_port + i visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size)) process = multiprocessing.Process(target=run_command, args=(visible_devices, dp_rank, vllm_engine_port)) processes.append(process) process.start() for process in processes: process.join() ``` 2. prepare the script `run_dp_template.sh` on each node. 1. Prefill node 0 ```shell nic_name="enp48s3u1u1" # change to your own nic name local_ip=141.61.39.105 # change to your own ip export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 export HCCL_BUFFSIZE=256 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000 export ASCEND_RT_VISIBLE_DEVICES=$1 export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \ --host 0.0.0.0 \ --port $2 \ --data-parallel-size $3 \ --data-parallel-rank $4 \ --data-parallel-address $5 \ --data-parallel-rpc-port $6 \ --tensor-parallel-size $7 \ --enable-expert-parallel \ --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --seed 1024 \ --served-model-name dsv3 \ --max-model-len 68000 \ --max-num-batched-tokens 32550 \ --trust-remote-code \ --max-num-seqs 64 \ --gpu-memory-utilization 0.82 \ --quantization ascend \ --enforce-eager \ --no-enable-prefix-caching \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_producer", "kv_port": "30000", "engine_id": "0", "kv_connector_extra_config": { "use_ascend_direct": true, "prefill": { "dp_size": 2, "tp_size": 16 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' ``` 2. Prefill node 1 ```shell nic_name="enp48s3u1u1" # change to your own nic name local_ip=141.61.39.113 # change to your own ip export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 export HCCL_BUFFSIZE=256 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000 export ASCEND_RT_VISIBLE_DEVICES=$1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_ASCEND_ENABLE_FLASHCOMM1=1 vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \ --host 0.0.0.0 \ --port $2 \ --data-parallel-size $3 \ --data-parallel-rank $4 \ --data-parallel-address $5 \ --data-parallel-rpc-port $6 \ --tensor-parallel-size $7 \ --enable-expert-parallel \ --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --seed 1024 \ --served-model-name dsv3 \ --max-model-len 68000 \ --max-num-batched-tokens 32550 \ --trust-remote-code \ --max-num-seqs 64 \ --gpu-memory-utilization 0.82 \ --quantization ascend \ --enforce-eager \ --no-enable-prefix-caching \ --additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_producer", "kv_port": "30000", "engine_id": "0", "kv_connector_extra_config": { "use_ascend_direct": true, "prefill": { "dp_size": 2, "tp_size": 16 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' ``` 3. Decode node 0 ```shell nic_name="enp48s3u1u1" # change to your own nic name local_ip=141.61.39.117 # change to your own ip export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name #Mooncake export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 export HCCL_BUFFSIZE=256 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000 export TASK_QUEUE_ENABLE=1 export ASCEND_RT_VISIBLE_DEVICES=$1 vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \ --host 0.0.0.0 \ --port $2 \ --data-parallel-size $3 \ --data-parallel-rank $4 \ --data-parallel-address $5 \ --data-parallel-rpc-port $6 \ --tensor-parallel-size $7 \ --enable-expert-parallel \ --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --seed 1024 \ --served-model-name dsv3 \ --max-model-len 68000 \ --max-num-batched-tokens 12 \ --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3, 6, 9, 12]}' \ --trust-remote-code \ --max-num-seqs 4 \ --gpu-memory-utilization 0.95 \ --no-enable-prefix-caching \ --async-scheduling \ --quantization ascend \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_consumer", "kv_port": "30100", "engine_id": "1", "kv_connector_extra_config": { "use_ascend_direct": true, "prefill": { "dp_size": 2, "tp_size": 16 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' \ --additional-config '{"recompute_scheduler_enable" : true}' ``` 4. Decode node 1 ```shell nic_name="enp48s3u1u1" # change to your own nic name local_ip=141.61.39.181 # change to your own ip export HCCL_OP_EXPANSION_MODE="AIV" export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name #Mooncake export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 export HCCL_BUFFSIZE=256 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000 export TASK_QUEUE_ENABLE=1 export ASCEND_RT_VISIBLE_DEVICES=$1 vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \ --host 0.0.0.0 \ --port $2 \ --data-parallel-size $3 \ --data-parallel-rank $4 \ --data-parallel-address $5 \ --data-parallel-rpc-port $6 \ --tensor-parallel-size $7 \ --enable-expert-parallel \ --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --seed 1024 \ --served-model-name dsv3 \ --max-model-len 68000 \ --max-num-batched-tokens 12 \ --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3, 6, 9, 12]}' \ --trust-remote-code \ --async-scheduling \ --max-num-seqs 4 \ --gpu-memory-utilization 0.95 \ --no-enable-prefix-caching \ --quantization ascend \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_consumer", "kv_port": "30100", "engine_id": "1", "kv_connector_extra_config": { "use_ascend_direct": true, "prefill": { "dp_size": 2, "tp_size": 16 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' \ --additional-config '{"recompute_scheduler_enable" : true}' ``` Once the preparation is done, you can start the server with the following command on each node: 1. Prefill node 0 ```shell # change ip to your own python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100 ``` 2. Prefill node 1 ```shell # change ip to your own python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100 ``` 3. Decode node 0 ```shell # change ip to your own python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100 ``` 4. Decode node 1 ```shell # change ip to your own python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100 ``` ### Request Forwarding To set up request forwarding, run the following script on any machine. You can get the proxy program in the repository's examples: [load_balance_proxy_server_example.py](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py) ```shell unset http_proxy unset https_proxy python load_balance_proxy_server_example.py \ --port 8000 \ --host 0.0.0.0 \ --prefiller-hosts \ 141.61.39.105 \ 141.61.39.113 \ --prefiller-ports \ 9100 \ 9100 \ --decoder-hosts \ 141.61.39.117 \ 141.61.39.117 \ 141.61.39.117 \ 141.61.39.117 \ 141.61.39.181 \ 141.61.39.181 \ 141.61.39.181 \ 141.61.39.181 \ --decoder-ports \ 9100 9101 9102 9103 \ 9100 9101 9102 9103 \ ``` ## Functional Verification Once your server is started, you can query the model with input prompts: ```shell curl http://:/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek_v3.2", "prompt": "The future of AI is", "max_completion_tokens": 50, "temperature": 0 }' ``` ## Accuracy Evaluation Here are two accuracy evaluation methods. ### Using AISBench 1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details. 2. After execution, you can get the result. ### Using Language Model Evaluation Harness As an example, take the `gsm8k` dataset as a test dataset, and run accuracy evaluation of `DeepSeek-V3.2-W8A8` in online mode. 1. Refer to [Using lm_eval](../../developer_guide/evaluation/using_lm_eval.md) for `lm_eval` installation. 2. Run `lm_eval` to execute the accuracy evaluation. ```shell lm_eval \ --model local-completions \ --model_args model=/root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \ --tasks gsm8k \ --output_path ./ ``` 3. After execution, you can get the result. ## Performance ### Using AISBench Refer to [Using AISBench for performance evaluation](../../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details. The performance result is: **Hardware**: A3-752T, 4 node **Deployment**: 1P1D, Prefill node: DP2+TP16, Decode Node: DP8+TP4 **Input/Output**: 64k/3k **Performance**: 533tps, TPOT 32ms ### Using vLLM Benchmark Run performance evaluation of `DeepSeek-V3.2-W8A8` as an example. Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details. There are three `vllm bench` subcommands: - `latency`: Benchmark the latency of a single batch of requests. - `serve`: Benchmark the online serving throughput. - `throughput`: Benchmark offline inference throughput. Take the `serve` as an example. Run the code as follows. ```shell export VLLM_USE_MODELSCOPE=true vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./ ``` ## Function Call The function call feature is supported from v0.13.0rc1 on. Please use the latest version. Refer to [DeepSeek-V3.2 Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#tool-calling-example) for details.